Kele Xu (Senior Member, IEEE) received the doctorate degree from Paris VI University, Paris, France, in 2017. He is currently an Associate Professor with the School of Computer Science, National University of Defense Technology, Changsha, China. His research interests include audio signal processing, machine learning, and intelligent software systems. He serves as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology and a Guest Editor for Science Partner Journal Cyborg and Bionic Systems. He has (co-)authored more than 100 publications in peer-reviewed journals and conference proceedings, including ICLR, NeurIPS, CVPR, ICML, TASLP, TAI, TMI, JASA, AAAI, IJCAI, ASE, ACM MM, and ICASSP. For the book General Audio Signal Processing with Deep Learning, he has taken the leading role in designing the overall structure, integrating interdisciplinary perspectives, and shaping the thematic direction of the chapters. Dr. Jisheng Bai received the Ph.D. degree in Information and Communication Engineering from Northwestern Polytechnical University, Xi’an, China, in 2025. From 2023 to 2024, he was a visiting Ph.D. student at Nanyang Technological University, Singapore. Since 2018, he has been a co-founder of Xi’an Lianfeng Acoustic Technologies Co., Ltd., China, where he has been actively engaged in transferring advanced acoustic signal processing methods into real-world applications. He is currently a Lecturer with the Center for Image and Information Processing, School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, China. His research interests focus on deep learning and audio processing, with particular emphasis on bridging fundamental algorithms with practical deployment in speech enhancement, acoustic sensing, and intelligent audio systems. For the book General Audio Signal Processing with Deep Learning, Dr. Bai has primarily contributed to the sections emphasizing application-oriented perspectives, highlighting how state-of-the-art deep learning techniques can be effectively adapted to solve real-world challenges in audio technology and industry practices. Dr. Boqing Zhu is a Research Fellow at the National University of Defense Technology, China, specializing in computational acoustics, underwater acoustic signal processing, and continual learning methodologies. He received the Ph.D. degree in Computer Science from the National University of Defense Technology in 2023. His research lies at the intersection of traditional signal processing and modern machine learning, with a particular focus on the foundations of deep learning, continual learning paradigms, and their applications to underwater acoustic sensing, audio synthesis, and audio-visual learning. For the book General Audio Signal Processing with Deep Learning, Dr. Zhu has mainly contributed to the theoretical underpinnings of machine learning methods, including continual learning frameworks, while also integrating domain-specific insights from underwater acoustics. Qisheng Xu is a Ph.D. candidate at the College of Computer Science and Technology, National University of Defense Technology, China. He received his M.S. degree from the same institution in 2024 and his B.S. degree from Wuhan University in 2021. His research interests include audio signal processing, continual learning algorithms, and intelligent computing. His recent work emphasizes audio representation learning, where he investigates how advanced machine learning methodologies can be effectively integrated with signal processing techniques to achieve robust and generalizable audio analysis. For the book General Audio Signal Processing with Deep Learning, Mr. Xu has primarily contributed to the parts focusing on representation learning for audio, bridging theoretical modeling with practical algorithm design. His role ensures that the book highlights not only cutting-edge deep learning models but also their connections to fundamental audio representations and real-world signal processing challenges. Yi Su received the B.S. degree in Communication Engineering in 2018 and the M.S. degree in Electronic Information in 2023. She is currently pursuing the Ph.D. degree in Computer Science at the National University of Defense Technology, Changsha, China. Her research interests include pattern recognition, data engineering, and multimodal signal processing, with a particular focus on audio-language modeling and cross-modal understanding. Her recent work explores how deep learning can bridge audio and language, enabling richer semantic representation and interpretation across modalities. For the book General Audio Signal Processing with Deep Learning, Ms. Su has mainly contributed to the sections on multimodal audio-language learning, ensuring that the book not only covers core signal processing foundations but also extends toward emerging directions where audio interfaces with natural language technologies. Dr. Mou Wang received the B.S. degree in Electronics and Information Engineering and the Ph.D. degree in Information and Communication Engineering from Northwestern Polytechnical University, Xi’an, China, in 2016 and 2023, respectively. From 2021 to 2022, he was a Visiting Ph.D. Student with the National University of Singapore, Singapore. He is currently a Postdoctoral Researcher with the Institute of Acoustics, Chinese Academy of Sciences, Beijing, China. His research interests include machine learning and speech signal processing, with a particular focus on speech enhancement and audio denoising techniques. He has received several distinctions, including the Excellent Paper Award at the International Conference on Ubi-Media Computing and Workshops in 2019, the Best Paper Award at the 19th National Conference on Man-Machine Speech Communication in 2024, and the Outstanding Reviewer recognition from IEEE Transactions on Multimedia in 2022. For the book General Audio Signal Processing with Deep Learning, Dr. Wang has contributed primarily to the sections on noise reduction.
